Supervised machine learning models are developed with the assumption that the training and testing data are independent and identically distributed (i.i.d.). As a result, such models can fail drastically when tested on non i.i.d. test data [1]}.
This severe drop in performance indicates poor generalization capabilities of the learned models, and addressing this fundamental challenge has become an important topic of research [2]}, [3]}, [4]}. Over the last few years, several classes of solutions, ranging from unsupervised adaptation approaches [5]}, new formalisms for domain-invariant learning [6]}, [7]} to data augmentation techniques [8]} and novel regularization strategies [9]}, have been proposed to improve generalization under covariate shifts. Furthermore, given the inherent limitation of using data from a single source domain to generalize under real-world shifts, methods that leverage multiple domains have also emerged [10]}. Commonly referred to as zero-shot, multi-domain generalization (ZS-MDG), this formulation assumes that labeled data from multiple source domains is available but with no access to the target domain.
